Entropic Measures of Complexity in a New Medical Coding System

TL;DR

This paper introduces entropic measures to evaluate the complexity of transitioning between medical coding systems, aiding preparation efforts by identifying concepts likely to pose challenges during migration.

Contribution

The paper proposes two novel entropic measures to quantify coding complexity and demonstrates their application using ICD-9-CM and ICD-10-CM/PCS mappings.

Findings

Higher entropy indicates greater transition challenges.

Entropic measures can guide training and documentation improvements.

Applicable to any two medical coding systems with mappings.

Abstract

Background: Transitioning from an old medical coding system to a new one can be challenging, especially when the two coding systems are significantly different. The US experienced such a transition in 2015. Objective: This research aims to introduce entropic measures to help users prepare for the migration to a new medical coding system by identifying and focusing preparation initiatives on clinical concepts with more likelihood of transition challenges. Methods: Two entropic measures of coding complexity are introduced. The first measure is a function of the variation in the alphabets of new codes. The second measure is based on the possible number of valid representations of an old code. Results: A demonstration of how to implement the proposed techniques is carried out using the 2015 mappings between ICD-9-CM and ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed in the context of clinical concepts that were likely to pose challenges regarding documentation, coding errors, and longitudinal data comparisons. Conclusion: The proposed entropic techniques are suitable to assess the complexity between any two medical coding systems where mappings or crosswalks exist. The more the entropy, the more likelihood of transition challenges. Users can utilize the suggested techniques as a guide to prioritize training efforts to improve documentation and increase the chances of accurate coding, code validity, and longitudinal data comparisons.